{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lesson 14.4 - Plotting with Seaborn: Plotting with categorical data\n",
    "\n",
    "*Facsimile of [Seaborn tutorial](http://stanford.edu/~mwaskom/software/seaborn/tutorial.html).*\n",
    "\n",
    "In the relational plot tutorial we saw how to use different visual representations to show the relationship between multiple variables in a dataset. In the examples, we focused on cases where the main relationship was between two numerical variables. If one of the main variables is “categorical” (divided into discrete groups) it may be helpful to use a more specialized approach to visualization.\n",
    "\n",
    "In seaborn, there are several different ways to visualize a relationship involving categorical data. Similar to the relationship between `relplot()` and either `scatterplot()` or `lineplot()`, there are two ways to make these plots. There are a number of axes-level functions for plotting categorical data in different ways and a figure-level interface, `catplot()`, that gives unified higher-level access to them.\n",
    "\n",
    "It’s helpful to think of the different categorical plot kinds as belonging to three different families, which we’ll discuss in detail below. They are:\n",
    "\n",
    "Categorical scatterplots:\n",
    "\n",
    "* `stripplot()` (with `kind=\"strip\"`; the default)\n",
    "* `swarmplot()` (with `kind=\"swarm\"`)\n",
    "\n",
    "Categorical distribution plots:\n",
    "\n",
    "* `boxplot()` (with `kind=\"box\"`)\n",
    "* `violinplot()` (with `kind=\"violin\"`)\n",
    "* `boxenplot()` (with `kind=\"boxen\"`)\n",
    "\n",
    "Categorical estimate plots:\n",
    "\n",
    "* `pointplot()` (with `kind=\"point\"`)\n",
    "* `barplot()` (with `kind=\"bar\"`)\n",
    "* `countplot()` (with `kind=\"count\"`)\n",
    "\n",
    "These families represent the data using different levels of granularity. When knowing which to use, you’ll have to think about the question that you want to answer. The unified API makes it easy to switch between different kinds and see your data from several perspectives.\n",
    "\n",
    "In this tutorial, we’ll mostly focus on the figure-level interface, `catplot()`. Remember that this function is a higher-level interface each of the functions above, so we’ll reference them when we show each kind of plot, keeping the more verbose kind-specific API documentation at hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "#sns.set(style=\"whitegrid\", color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(sum(map(ord, \"categorical\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical scatterplots\n",
    "\n",
    "The default representation of the data in `catplot()` uses a scatterplot. There are actually two different categorical scatter plots in seaborn. They take different approaches to resolving the main challenge in representing categorical data with a scatter plot, which is that all of the points belonging to one category would fall on the same position along the axis corresponding to the categorical variable. The approach used by `stripplot()`, which is the default “kind” in `catplot()` is to adjust the positions of points on the categorical axis with a small amount of random “jitter”:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"day\", y=\"total_bill\", data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `jitter` parameter controls the magnitude of jitter or disables it altogether:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dCycvDJoZQX6JAwYYjiWyZ2XBnpysMVF0eAVMZAJKKTy6+lEo+Hcn9CovHlvzmOZU5tXzoQfh7OuffWTv1g29HntUc6Lo8AqYyAR8yocqd5Whr8JVEebRlDhwIAZ8sBieoiI4srIgIY4osgJeAROZgN1mxzkDzzH0zT5mtqY01iAicHbrZtniC/AKmMg0FkxYgCFZQxoWYpzZ/0zdkUxLud0ofOBBVH2xDMljxiBnwQLYUlN1x4oYT8QgIsv54RdXonrZsoZ24uDB6P/2WxoTBWnVpOSYDkGISJKIrBCRdSLyvYjcFujvJyLLRWSbiPxbRKy3hIXoKDtQfQBzFs/B8S8djyuXXImyOu6yF071l18a2vVbtsBbW6spTfRiPQZcD2CqUmo0gDEApovI8QDuA/CQUmoQgDIAc2Ocg8j0rvjgCqwuXI1qdzW+3vc1rlpyle5I5hVi4x0rbsYT08TK7/CtXWfgSwGYCuC1QP9zAH4ayxxH2zc7SzD94f9ixC0f4vevrgu5QxpRJOq99fix6kdD3+bSzZrSmJ8jJ8fQtqWnW24jHqCFm3Ai8i6AsIPESqmzW/oLRMQOYBWAgQD+AmAHgENKKU/gIXsB9Azz3CsBXAkAffr0aemviotalxdXv7AKh2r8K3FeXbUX3TIS8fvTh2hORlbmEAcE0jAPGPBvUUmhefbvN7R9lZXw1tfDbrEi3NK/cJu35FdKeQGMEZHOAN4EMDTUw8I8dxGARYD/JlxbsxwNWw9WNhTfw77dxbE6ahu7zY5ZA2fhre2NN5IuG3aZxkQWZJEJBUdqtgArpT4/Wn+RUuqQiCwFcDyAziLiCFwF9wJgmYXvg7qnIT3Jgco6T0PfmD6dm3kGUevcccIdOLnXyVhduBon9DgBk3ryPLhw0qdOReWSJQ3tpFGjYE+y3tmMzY4Bi8h6Efku3FdLf7iIdA1c+UJEkgGcCmATgM8AHJ5lPgfA2237z4iflAQHHrvoWPTNSoHdJjhjVC5+c8og3bHMrboEKPgW8NTrTmJqSinUeevgUz7Ueqx3Rz+eej74ALKv/RWSRoxAlzlz0PfZZ3RHikqz84BFpNmjHpRSe5r9w0VGwX+TzQ5/sX9FKXW7iPQH8C8AmQDWALhUKdXsT6cZ5wErpSy5B2lcrXkBeO8GwFsPpHUHLn0dyBmpO5UpLfx2If658Z8N7V8f+2v8YtQvNCaiNmhVYeBCDIoddy1w/zFA/RF7Ggw81V+EycDr82LCSxNQ7228Duma3BWfnv+pxlTUBm1fiCEiXwR+rRSRiqa/Ho2UVlVa7cLGfRXw+azxBqZFXbmx+ALAoQI9WUxORJBoN97BT3JYb0wzXnzV1Thw193YefYs7LvxJnhKS3VHikpLN+FODPyaHp841vDUF7tw3+LNcHl96N81Fc//z3j06pKiO5b5pOcAfSYBP3zV2Df8nPCP78BsYsM1o6/Bfd/eBwAQCM+Ha8aBO+9C+ZtvAgDqt26F58AB9Hn6Kc2pItfqhRgicpyI/FpErhORY2MZysxKqupx7+JNcHl9AICdRdV49JNtLTyrA7vgBWDC1UC/ycC0O4GT5ulOZFrnH3M+Tup5ErKSsjC973Sc0Z/H0odT9dlnhnb1V1/BV2+9m7ytmuktIjcDOA/AG4GuZ0XkVaXUnTFLZlL7y+vg9hqHHX4ordGUxgJSs4AZ9+lOYQkXvn8hth3yv5kv3rMYRR8W4Znp1ry7H2sJ/fujdvXqhrazd29Lnorc2ivgiwCMU0rdopS6Bf65vJfELpZ5Dc3NQF6WcbhhxohcTWmovaj31DcU38NWHVylKY355fzpj3D26AHAfxxR7h23W3JGUmvXOu4GkASgLtBOhH9JcYdjtwn+OXcCHv54GwrKanDGyFxcPrHZ2XpELXLYgpci28W6G43HWtLQoRiw5CO49+6FMzfXkle/QMt7QTwG/zLhegDfi8iSQPs0AF/EPp459c5MwQPnj9Ydg9oRu82O6XnTsXj34oa+CwZfoDGR+YndjoS+1r74aWkhxpzmnqyUeu6oJwrDbPOA69xelNe60T2DU4Wa5XEBa18ECjcBg6YBg07VncjUlhYsxWc/fIaZ/WdiQu4E3XEoeq0aD2lpGlqrCqyIvK6U+llrHtsevL5qL25+ewOqXV6M6d0Jf798HLqmW2sXprh562pgQ2DhxYongbMeBcY2+77eYe04tANPrnsSG0o2YHfFbvRK74WeaSE3CqR24mjtB9z/KP05plde48a8179Dtcu/B/DagnLc8d73mlOZVHUJsOENY9+3f9eTxQIWLFuADSUbAACrC1fj9q9v15zI3DxlZaj6/HO4DxbqjhK1o7XhaIdZDramoAzeJqvfvtxeoimNydmd/i+vq7HPab2DE+PB7XNjU+kmQ9+awjWa0phf1bIvsPe666Dq6gCHAz3uuhOdZs3SHSti1jvDQ7MuKcF3W9MSuXF2SEkZwKRfN7btCcBJv9eXx8ScNiecNqehj7Mgwit84AF/8QUAjwcHF/4ZyufTGyoKR6tyWG8CXpRG9eqEvKwU7C5pXHxxzckDNCYyuVP+BAyeARRuBPqfDHQ2x8kmZuP2ueH2GTf6r+f2nWF5iosNbe+hQ1Aej+Wmox2tAvyHo/TnmJ6I4JWrJ+JvS3eioKwGM0fm4Jxje+mOZW698v1fFJaEuIbxwXpXdPHS+aezUPKPxr0fMmbOhM1ixRdoeR7weoQe3xX4z9wcBf83H8Ugm2l1S0/CzWcN0x2D2pMQP2Wq49xaiVjX66+HIzcXNctXIGnECGTOuVx3pKi0dAV8ZlxSEHVwdpsdyfZk1HobT8LITMzUmMjcxG5H5iWXIPMSa++I0NI84GZPvCCio0NEMDRrKFYXNm4wM6rrKI2JzE0phYr33kP18uVIHjECnWfPhjisdzO8tbuhHQ/gMfhPNE6A/4ihaqVURgyzEXUYHp8H64vXG/rWFq3VlMb8ih57DCV/fQIAUP7a66hduw497r1Hc6rItXYa2uPw74i2DUAygP+FvyAT0VFgFzvSE4znHnRK7KQpjfmVPvOsoV3+9ttQLlfoB5tYq+cBK6W2A7ArpbxKqWcATIldLKKORUQwI2+Goe+s/mdpSmN+qunm60rB5/XqCdMGrS3ANSKSAGCtiCwUkesBcEkT0VH0xnbjsu0XNr2gKYkFhBjvlUTr7cfS2gJ8WeCx1wKoBtAbwLmxCkXU0dR6alHrqTX0ldZZ86DJeAi5+brHE/8gbdTaAvxTpVSdUqpCKXWbUuoGdOApausKDuH8v32Nifd8gtvf3QiXhxPmqW2SHclBpyJ3SuAYcFj2Jsu0bTZLnojR2gIcav/AK45iDsuo93gx97lvsWJ3KfaX1+HpL3fhiaUd8nAQOsruOOEOOGz+j9YJ9gTcN5ln6YUjKU1OIXc4AJv1trZpaSXcRQAuBtBPRN454rcyAHTILcC2HKhEcZXxbuuX24vxm1MHaUpE7cWMfjOQm5qLZXuX4bS80zAkc4juSKblKyszdrhc7a8AA/gKwH4A2QAeOKK/EsB3sQplZv2yU5GSYEeNq/GO67AenA5Nbffiphdx74p7AQD/2PAP3HXiXTizf4cd6WteiBkP7uJiJHTtqiFM9Jp9y1BK7VFKLVVKTQSwGUB64GuvUsp6I95HQXqSEycOzG5oO+2Cs0b10JiI2gOf8uGR1Y8Y2g+sfKCZZ3RsQbueicCZnR36wSbWqmt2ETkPwAoA5wE4H8ByEZkdy2BmdaC8Dh9vOtjQdnsVnvt6t7Y81D4opYJmQZTUdshRvlZJmznT0E7Oz7fkTbjWLp7+I4BxSqlCABCRrgA+BvBarIKZVVmNC00OxEBJNfdtpbZx+YJXcXE3tPBqvvzS0K5buxbK7YY4nWGeYU6tHbW2HS6+ASURPLddGZKTjhE9jWO+s8dyP2Bqm2RHMlIdxrVN2cnW+0gdL76qKkNbeb3t8ibcYYtF5EMALwfaFwD4T2wimZuI4J//MwFPfbErsCF7Lk4fnqM7FrUDj5/yOH7z2W9Q4apAZlImnjj1Cd2RTCvo+CGfz38F3HR+sMm1tgArAE8COBH+zdgXATg+VqHMrktqAn53+mDdMaidyc/Jx5cXfYlKV2XQxjxkJIkJxv0gHA7YkpL0BYpSa6/ZT1NKvaGUukEpdb1S6k0AM1p8VjtW5/aisKJOdwxqh1h8W6YqjUMQ8Hjgq7Pez2NLCzGuAfBLAP1F5Mh5v+kAvgz9rPbv78t2YuEHm+H2KvTukow3fjkJXdOt9+4bF7VlwBcPAYWbgEHTgHH/C1jwbnW8HKo7hK1lWzE0aygLcXNU8A1Kn9ttuavgloYgXgKwGMA9AOYf0V+plOqQO4WUVrtw9/ubGu5PF5TV4pcvrsarV0/Smsu0/n0ZsHuZ//ttHwF1h4DJPJo+lCV7lmDBsgWo99YjxZGCh6c8jIk9JuqOZUq29HT4KisbOxwO2NPS9AWKUksLMcqVUruVUhcFFmUc/uqQxRcA/ru1KGhy0OYDlSEf2+FVHmwsvoet4RaL4dyz/B7Ue/3jmjWeGiz8dqHmRObVY+F9jVtS2mzo/seb2vU8YAo4vn/wQYl5WSkhHkmwh/jfq64i/jkswOPzoKi2yNC3p4JHMoaTdsIJyJw7F9Wff47ksceh05nWXLJtvYlzmuV0SsZVk/vj8Htt5xQnHr/4OK2ZTMvrDu5L5LhmKKEWXagQ45zkd/C+hSh98knUb96MQy++hH3z57f8JBPiFXAUFswciqtOGoCDFXUY3D0dNpv1PvrERXoO0GcS8MNXjX1jLtaXx8ScNifsYodXeQ19FFrF4sWGdtWnn8HncsHWdI8Ik2MBjlJmagIyU631j63FBS8A//0zUBSYBTHhGt2JTMntcxuKLxDm1AcCADh79IC3tPFWlKNrV8stQwZYgCnWUrOAGffqTmF6TpsTWUlZKKlr3ICna4q1tlaMp8y5c7Hv//4PCKyI63LFHEu+YXEMmMgEPD4PyuqMm4w3bVOjoocfbii+AFDytyc1pokeC3AUymvceOTjbfjdq+vw6eaDLT+BqAUOmyPoijcnlXuMhOP+4QdD21deDm+l9aaDsgBH4fJnVuChj7fitVV78T/PrsQ76/bpjmRelQeBd34N/OM04PM/A94OuY9/q8wfPx+JNv/BnCmOFPx+HBeshCPJycYOmw22pufEWQALcIQ2H6jAuoJDhr5Xvi3QlMYC/nUxsPo5YO8K4LM7gaV3605kWl7lbZiOpqDga7rjFzVI6NvX0LZnZ1tuJzSABThinZKdaDrrrHOK9e6+xkXFfuDHlca+Te/qyWJySincvfzuho3Zaz21DefDUbD6LVsMbW9hIbwW3IyHBThCuZ2SMXVIt4a2wya4YlKevkBmltwFSOpk7OvST08Wk/MqL0rrjCv8d1fs1hPGCkJ8OlAhDuo0OxbgCFXVe/DVjsapQh6fwuINBzQmMjFnEjDzAcAZOOmhUx/gtNv0ZjIpty941SCPJIqMWLAAx3QesIj0BvA8gBwAPgCLlFKPiEgmgH8DyAOwG8D5SilLzLnZWVRlOJIeAJZtKwrzaMKo84BjTgcO/QB0GwrYrDdOFw8C681h1cnRuzc8BY33XiQ9HfaMjGaeYU6xvgL2APg/pdRQ+E/Q+JWIDIN/a8tPlFKDAHwC41aXpuYIsey43s2bJc1KygByRrD4NoM33CLT95mnkdDPP5zlyMlB3+ee1RsoSjG9AlZK7QewP/B9pYhsAtATwCwAJwce9hyApQD+EMssR0t6UvANt4FdU0M8kqj1bMLRwEg4c3LQ+YLzUf3NciSPHIHEfta8txC3pcgikgfgWADLAXQPFGcopfaLSLcwz7kSwJUA0KdPn/gEbUHX9EQkOGxweRqvWLLSEzUmovYgwcF9RSJReP8DKH32WQBA9dKlcO3YgZ4PPqg3VBTi8rYrImkAXgfwW6VUqzeEVUotUkrlK6Xyu3Y1x7r4rQcrDcUXAHYVV2tKQ+1F0414qHnlb71laFd88CF8LpemNNGLeQEWESf8xfdFpdQbge6DIpIb+P1cAIWxznG09MlMCbpdYrPgJiBkfnbhmHk4QcVWBOKw3t5iMS3A4t+e6CkRT7fDAAAUqUlEQVQAm5RSR34+eAfAnMD3cwC8HcscR1NBaW3Q5CCXlzdQqG2cNicGdxls6BuXM05TGvML2vnM64VyhzgAwORifQV8AoDLAEwVkbWBr5kA7gVwmohsA3BaoG0JPToHn7pq5xUwHQUPTXkIE3ImIMWRgsm9JuPuE7lsO5y0qVMN7ZTx42FLtN69mFjPgvgCCDvB8ZRY/t2xsr88eLmjy4ITwMl8eqX1woVDLsTw4uEY230s9wNuRs7Nf4IkJqBm+QokjRiO7vMX6I4UFesNmmiWlhT8ku0rs94adDKfh1Y/hGc2PAMAeHrD07hh7A34+Yifa05lTvb0dPS48074qqthS7XuNFBOPozQsq3B9wsP1Vpv7InMxevz4uVNLxv6Xtj4gqY05ufaswc7zzkXW8bmY/vp01G74XvdkaLCAhyhnIzkoD47D+WkNhIRuLzGO/sVrlbP2OxwfrzhBtRv2gQAcO/Zg4Jf/UpzouiwAEdoYLe0oL5kJ19GahulFHwwzqap83JoK5y67zca2t6DB+GtrdWUJnqsHBFa22QzdgDgLDRqq3pvve4I1qest3scC3CETh+RA3uTEYdx/TL1hKF2I8VpveN0TMeCGxqxAEcoJcGBRy88FmkJdtgEGNEjA3+79DjdscjiyuvKdUewllBz73kkUcfw/Dd7UOXywqeA7/dV4POtxbojkcUlOqy3iEAnaXoAp90Oe9ODOi2ABThC2w5WYvmuxqNjFIBHPt6qLxC1Cw5b8PxybtIenqpusgGW1wtvVZWeMG3AAhyhgxXBN0sOVPBuNbWNw+bAqOxRhr6Tep2kKY01Kd6Ea//6ZAbfLBnQNXhqGlEklFLYfmi7oW9D8QZNaazJ5rTe6eQswBHqk5WCEwZmNbQFwG9PHaQvELULbp8bNZ4aQ19JXUmYR1MoPu6G1v7Vub34envjD4YC8PKKH/QFsgoLfjyMJ4fNEbT/b7LDejeVtOJ+wO3fxxsPoOlsw/fX81j6sLZ/AtzXD7itM/D4eKDKMnvvx5VNbOiR2sPQ1y/DmuecxYM0nfFgs8HBWRDt37q9wfM1vT5e3YXk8wIvXwTUBmaNFG8B/nmu3kwm5fF5UFBVYOjbUrZFUxrzU/VNbob7fPDVWe9mOAtwhCb0zwrqc3IzntAOfAc0XWJbtFlPFpNTQees8Jy4ZoVY9aa4Eq79y8sKngXhtN4CnPjoEuIjtI0vViihzn/jmXDNCDHea+MYcPv3/nf7gvrqPRyCCMkV4rTolOBPEBR6DqtHeTQksYbU8eMN7YSBAyEJCZrSRI8FOEIXju8T1JedxmWkIWX0ALIGGvsGz9CTxeRqvdbbSlEn1969hrZn/34oCx4NxgIcIbvNhqZDvtwNLQwRID3X2Nept54sJpdo45t4JDyFxtk0vpoa7obWERwor0PTSQ/FVdzLNaSqImD3MmPfd6/oyULtm1K8CdcRDMvNQP9s4yGAZ4zMDfPoDs6ZDDRdTJDCTwuh2EPcnLQJfzzDsXfubGhLcrIlj6Xnv3CEbDbB83PH4/z8XpjYPwt3/HQELpuYpzuWOSWmASfPb2w7U4GTrXl8eKw1PQ8OAHzKeld08eIpKzO0VW0tfK7g19DsrDdvwwQcNhsSHP6vRDvfw5p14m+BwTP983/zTuQVcBgJ9uA7+NyOshkhiq0VhyBYgCPk8ylc+tRybC/07z36+dYieHwKF08Inh1BAV2P8X9RWKEWXYTaI5gCQu0tYsECzMu3CG3cX9FQfA97dWVBmEcTtY7T5kSXpC6GvtxU3lsIJ2jOrwi3o+wIbCFesYKymuBOogh4fV5UN1m4UuWy3gkP8RJ0Ey4hIfQ5cSbHAhyhXcXBq7vq3db76EPmIiJBwxB1XuttLhMvPo9xlaBSiodydgSjenYO6strMi2NKFI+5QsqwPVNNzKiBr7SUmOHywVfrfVWE7IAR6h3ZgomD8puaNsEuG3WMI2JLOC/DwAvXQhsfl93EmrPLLjpPwtwhGpdXny9s/FEDJ8C/vk1T8QI628nAp/eDmxdDPzrYmDJrboTmZJd7EiyJRn6uiR2CfNosmVkGDucDti4IXv79/nWQri9xnfaTzfzlIeQyvYAB9Yb+1Ys0pPF5EQELmWc21rhrtCUxvzs6emGtiRYbxUcwAIcsa7pwf/QLo/1dmGKC1+o14U3LEOpclUFrXwLtTqO/DzFxYa2qq6G8lhv+04W4AjtKQmecsZZEGFk9QcyBxj7Rl+sJ4vJpTiCN/qnCHEMuP3bWRQ8N5Pltxm//AYYfyXQZyJwxkPAmQ/qTmROIaawcilyeM6+fQ1te1YWN2TvCKYc0y2oL9HBlzGswo3A3m+BfWuAnZ8CNaUtP6cDcnvdQX2hzokjP1+V8UJI1dVBLLgQg4vNI5SUGDzZO8lhvX/4uPD5gFcuBw7t8bc3vQskdQJm/UVvLhPizmeR8Rw4YGj7qqvhramBPcVaQzm8dIvQpn3Bd6ZrXPzhCalyX2PxPWzHZ3qymFyyMxkJNuNH6HRnephHkzTd+5d7QXQM2SFmQSRwCCK0pE7BfdzfIKxpedMM7XMHnaspifllXXWVod159myIBQswhyAiNKZ38FLkYbm8UgmpPPgEabi5v0EoPuXD0oKlhr4le5bgd+N+pyeQyXW9+iokDxuK6uXLkTxiBNKnT9cdKSoswBEqrAxen19eZ735h3GR3h3+2/tH3ExK5bH0oSilgvZ+qHYHb/xEjdImT0ba5Mm6Y7QJPztHqLAi+AqutJoT5kNK7gyMucTYN/0+PVlMzm6zB92I425o7R8LcIR6dgleb56WwA8SYdWWGNsVIYYlCDWeGu6G1gGxAEeouj54ea3Xgitw4qKqCNiy2Ni3+nk9WUwuxZECW5MfR6fNejeVKDIswBGqdQUX4Kab81BAqGPpk7nDVyhunxu+JmsqvSH30qD2hAU4Qj1CDEEM65ER4pGExDTgpHmNbWcKMIXH0ofCZccdEwcvI9SzczKyUhNQcsSNt/y+vKoL6yc3GI+lT81u+TkdkF2sd5wOtR2vgCO0+UCFofgCwBfbijSlsYhuQ4DhP2XxbUa9J/iGW9MhCWrkLizElhNOxKYhQ7F53HjUbtykO1JUYlqAReRpESkUkQ1H9GWKyBIR2Rb41VKXj0578EfF7SF2SKMj7FwKfPMEULRVdxLT8oLjvZHYMe10+Er8M2xUZSV2z56tOVF0Yn0F/CyApktU5gP4RCk1CMAngbZlrPuhPKivkgsxwvvwJuD5WcAH84G/Hg9s/o/uRKaU6kwNmgWRaLPmKQ/xoOqazJH2+eA6eFBPmDaIaQFWSv0XQNP9B2cBeC7w/XMAfhrLDEdbn6zg3ZbsNo7khFR7CFj+ZGNbeYEvuB9wKEoppCYYT9fOTuGQTSTsTc+JswAdlaO7Umo/AAR+Dd5g18RSQ2xHmZnK+ZohKZ+/6B6Jx+yE5FVe1HqMx6o3bdMRHME/hza79W5kmvrSTUSuFJGVIrKyqMgsN7qCx4CTnNb7h4+LlExg9EXGvuN/qSeLyTlsDozMHmnoG911tKY0FhBq6r0FC7COaWgHRSRXKbVfRHIBhD1SWCm1CMAiAMjPzzfFaocuKQmwif84+sOG5Fjvo0/cnP0Y0H+K/2SMQacBfSfpTmRKSinsOrTL0Lfz0E5NaSzAG3zT0ldbC3tamoYw0dNxBfwOgDmB7+cAeFtDhqjldErC1Sc1HjSZnZaA66YO1JjI5Gx2YNR5wKm3sPg2w6d8qPEYD3ytdFdqSmN+tibH0sPhgM1ip2EAsZ+G9jKArwEMFpG9IjIXwL0AThORbQBOC7QtZd70Ifj4hpPw7M/HYdm8qRjUnfsBU9vYbXacM+gcQ995x5ynKY355dx6K3DEze+u118PseDN8JgOQSilLgrzW6fE8u+Nh4Hd0jCwm7U+7pC5zR8/H4MzB2ND8Qbkd8/Hmf3P1B3JtFInTUTa1KmoWbECScOGodMMa27ILsoiO3nl5+erlStX6o4BAHB7vHjkk+3YVliJKyblYeIAThcKq3wv8PFtQOEm/xjwyQsAh/WODydz2Xv99ahc/EFDO/m445D30osaEwVp1eYe3AsiCpMXfob9Ff6lox9+fxC3nT0ccybl6Q1lVv+6GNi/zv/9wfWAzwNMu0NvJrK8mq++NrRrV6+Gr64OtqQkTYmiY71BE82+3F7cUHwPe3AJl9iGVLGvsfge1nR/YKIoJA4bamgn9O9vueILsABHrKC0JqivxsWlyCGlZAHJmca+7GP0ZKF2JfeWW5A41F+EE/r2RY/7LHcvHwCHICJ2fP/gQyX7Zlpv+ktcOBKBsx4B3rkWqCsHsgYCp92uO5XpuX1unobRgoS8PPR/8w14KypgS0+HiDX3U2YBjlBediqmDeuOjzb6N/5w2AR3njOyhWd1YMPO9t98q9gHZPYHLPqDEg/byrbhxi9uxObSzRjddTTu+ck96J3eW3csU7Pi/g9H4hBEFGaOzEFyYPnxMd3TMbJnJ82JTM6ZDGQNYPFtweHiCwDritbh9q/5aaG9YwGOUHW9Gze8sg61bv9SyI37K/DLF1dpTkVW5/a5G4rvYRuKN4R5NAH+pcc1a9bAW2ndFYMswBH6aONBwz4QALD6h0N6wlC74bQ5kZeRZ+gb2JlL3MOpWbUK206egj0XXYxtk09CxZIluiNFhQU4QmP7BB/gkdvJetNfyHyq3FXNtqnRwXvuha/cfziCqq3FwTvvglUWlR2JBThCfbJSMXtsr4Z2stOGRy48VmMiag88Pg9K64xnFxTWhN0osMNz79tnaHuKiqDcbk1posdZEFG4/7zR+O2pg/BjWS2O7dMFCQ6+j1HbOGwOnNLnFCzZ0/hRenqeNfc3iIeMGTNQ9mLj0uO0qVNgS7DeEncW4Cj16pKCXl04/5eOnjtPuBN90vtgQ/EGjM0Zi7kj5uqOZFrd/zAPjuwsVC9fgeQRw5F19TW6I0WFm/EQER19rZpzyc/ORESasAATEWnCMWCKrfoq4JsnAmfCTQPGhNujn6jjYQGm2Hr1CmB74M7+928ANcXApOu0RiIyCw5BUOxUFTYW38PWvqQnC5EJsQBT7CSkAs4mU/VSgrfzJOqoWIApdhJSgSk3oWFGTkI6MPWPWiMRmQnHgCm2Jl0LDJkJFG0B+kwEkjvrTkRkGizAFHuZ/f1fRGTAIQgiIk1YgImINGEBJiLShAWYiEgTFmAiIk1YgImINGEBJiLShAWYiEgTFmAiE9lVvgvv7ngXeyv36o5CccCVcEQm8cqWV3DnN3dCQcEudtw7+V4ezNnO8QqYyASUUnh8zeNQ8J/R6FVePL7mcc2pKNZYgIlMwKd8qHZXG/qqXFWa0lC8sAATmYDdZsc5g84x9M0+ZramNBQvHAMmMokF4xdgaOZQrC9ej/ycfJzR7wzdkSjGRCmlO0Or5Ofnq5UrV+qOQUTUGtKaB3EIgohIExZgIiJNWICJiDRhASYi0oQFmIhIExZgIiJNWICJiDRhASYi0oQFmIhIExZgIiJNWICJiDRhASYi0sQym/GISBGAPbpzNJENoFh3CIvga9V6fK0iY8bXq1gp1eJxJpYpwGYkIiuVUvm6c1gBX6vW42sVGSu/XhyCICLShAWYiEgTFuC2WaQ7gIXwtWo9vlaRsezrxTFgIiJNeAVMRKQJCzARkSYswE2ISJaIrA18HRCRHwPfHxKRjbrzWYGIeI94DdeKSF6Ix/QQkdfin848ROQmEfleRL4LvE4TmnnsFSLSI575zCKS18lqeCx9E0qpEgBjAEBEbgVQpZS6P1BE3ov2zxURh1LKczQyWkCtUmpMuN8MvBb7AMyOYyZTEZGJAM4EcJxSql5EsgEkNPOUKwBsALAvDvFMI4rXyVJ4BRwZu4j8PfBu/JGIJAOAiCwVkfzA99kisjvw/RUi8qqIvAvgI32x9Wv6WohInohs0J1Lo1z4V0vVA4BSqlgptU9EbhaRb0Vkg4gsEr/ZAPIBvBi4AkzWmjy+wr1OuwPFGCKSLyJLA9/fKiJPB34md4rIr/VFbxkLcGQGAfiLUmo4gEMAftaK50wEMEcpNTWmycwl+YjhhzeP6O+Ir0U4HwHoLSJbReSvInJSoP9xpdQ4pdQIAMkAzlRKvQZgJYBLlFJjlFK1ukJrEO51as4QAKcDGA/gFhFxxjRhG3AIIjK7lFJrA9+vApDXiucsUUqVxi6SKYUbguiIr0VISqkqERkL4CcApgD4t4jMB1ApIvMApADIBPA9gHf1JdWrmdepOe8HrpjrRaQQQHcAe2McNSoswJGpP+J7L/xXKADgQeOniaQmz6mOdSgL4WtxBKWUF8BSAEtFZD2AqwCMApCvlCoI3INo+v9ThxPidZqD5n/mmv6cmrbOcQji6NgNYGzg+w57Y4laT0QGi8igI7rGANgS+L5YRNJg/H+pEkB6vPKZRZjXaQ+MP3OtGQo0JdO+M1jM/QBeEZHLAHyqOwxZQhqAx0SkM/xXc9sBXAn/vYX18BeYb494/LMA/iYitQAmdqBx4HCv01AAT4nIjQCWa8zXJlyKTESkCYcgiIg0YQEmItKEBZiISBMWYCIiTViAiYg0YQGmDiOwT8DvdOcgOowFmIhIExZgatcCe8luEZGPAQwO9P0isOPYOhF5XURSRCRdRHYd3rhFRDICO26ZdiMXsj4WYGq3Apu4XAjgWADnAhgX+K03AjuOjQawCcBcpVQl/PsNnBF4zIUAXldKueObmjoSFmBqz34C4E2lVI1SqgLAO4H+ESKyLLCxyyUAhgf6/wHg54Hvfw7gmbimpQ6HBZjau1Br7Z8FcK1SaiSA2xDYTUsp9SWAvMCes3alVEfeMJ7igAWY2rP/AjhHRJJFJB3AWYH+dAD7A+O7lzR5zvMAXgavfikOuBkPtWsichOAy+HfwnAvgI3w70s8L9C3HkC6UuqKwONzAOwCkKuUOqQjM3UcLMBERwicvzZLKXWZ7izU/nE/YKIAEXkMwAwAM3VnoY6BV8BERJrwJhwRkSYswEREmrAAExFpwgJMRKQJCzARkSb/D0XIt6DtKgXcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"day\", y=\"total_bill\", jitter=False, data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second approach adjusts the points along the categorical axis using an algorithm that prevents them from overlapping. It can give a better representation of the distribution of observations, although it only works well for relatively small datasets. This kind of plot is sometimes called a “beeswarm” and is drawn in seaborn by `swarmplot()`, which is activated by setting kind=\"swarm\" in `catplot()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"day\", y=\"total_bill\", kind=\"swarm\", data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the relational plots, it’s possible to add another dimension to a categorical plot by using a `hue` semantic. (The categorical plots do not currently support `size` or `style` semantics). Each different categorical plotting function handles the `hue` semantic differently. For the scatter plots, it is only necessary to change the color of the points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"day\", y=\"total_bill\", hue=\"sex\", kind=\"swarm\", data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike with numerical data, it is not always obvious how to order the levels of the categorical variable along its axis. In general, the seaborn categorical plotting functions try to infer the order of categories from the data. If your data have a pandas Categorical datatype, then the default order of the categories can be set there. If the variable passed to the categorical axis looks numerical, the levels will be sorted. But the data are still treated as categorical and drawn at ordinal positions on the categorical axes (specifically, at 0, 1, …) even when numbers are used to label them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2bL1OmTeX2HHjbI5KFaZTEKrSO3bhGO9vep//bP4Ppy+dLvL4ihMrmLJvCrmWtDKfFzePeXHzyjrMUnX2m/H5yRfg0rLlZBzUcqLljVt/xRtjAoBlgH/e95piWdarxpj6wASgKrAJuM+yrEx3xqI806mLp7hz1p1cyLwAwOR9k5k6ZCrhfuFsOLOBAJ8A4lLiijwvLrnouYrE+HgXPedd9Jyyl7v/xsoAbrQs66IxxhdYYYyZCzwLvGNZ1gRjzMfAKOAjN8eiPNDsw7Pzky/A2fSzTD8wnRkHZ3Dg/AEAOlTvgI/xIduSEaPB0LN2T1viLS1VH3yQiz8vw8qUcU1gu3acfOUVDIaqox4i9IYbrvAKFVv6pSz2rjlNdlYOTbpEl9sGnW5NwJascXNUvvbN+7CAG4EReee/Av6CJmDlBoE+gUXO7UzamZ98ATbGb+Txto+z/vR6snOzubf5vbSKalWWYZa6oPbtaTB7FhcXL8bKzSX+329KOyogdfNm6k/9kYDGjW2O0j0y07OZ/MZ6UhLTAdi84Ch3/rEzoVXLXxJ2+xywMcbbGLMFiAcWAAeB85ZlOSaojgO1LvPc0caYDcaYDQkJWjpQ/XqDGw6mXli9/OPGVRoTFRhV5LqY4Bg+u/kzvur/FX3q9inDCN3Hr04dqo4ciZWenp98AcjJ4eLPP9sXmJsd3pKQn3wBMi5ls2f1qV94hn3cfpvXsqwcoK0xJgKYCjQr7rLLPHccMA5kI4bbglSVVphfGJMHT+bn4z/jbbzpUbsHh5IPMWHvhPzi7BH+EZV6J5xf/fpFzvk3aGBDJGXDeBfdA+FVzLnyoMzW2ViWdd4YsxToCkQYY3zyRsG1gZO/+GSlrkKATwA317s5/7hp1aZ82e9LJu+dTKBPIPc2v5dw/3AbI3Sv0D59CB8yhOQZM8AYwofeSkglngOu3yaKqjWDOXvyEgBB4X407RZjc1TFc+tWZGNMFJCVl3wDgZ+AfwEjgR8K3ITbZlnWh7/0WroVuYyd3AIHFkBUU2gy0CPqQ1R22QkJYAw+kZF2h+J2WRk5HNwUT3ZWLtd0qE5AsG9Zh1CiIbe7R8AxwFfGGG9kvnmSZVmzjDG7gAnGmNeBzcBnbo5D/Rq7psPkByBvXSztR8It79sakrp6PlFF574rK19/73I76i3I3asgtgHtijl/COjszu+trsKqD5zJF6RX3E1/kTZGSqlSU3H3WqrSlZMNx9ZAUDW7I1HKY/xiAjbGzOQyKxQALMu6pdQjUmXvYjx8MQCS9stxvR7SFSN/CuI+Hf0q5QZXGgH/X5lEoey19mNn8gWIWwa3/Q+Sj8lNuMb97YtNlYrshARZBYEh/NYh+FTTv3TKg19MwJZlVd7V2srpwpmi5/xD4frnyj4WVeqyExM5dNtt5CQkAnD2q6+oP30aPlWq2ByZ+sW1RcaY7caYbZf7KKsglZu1Ho7LqpmQaKhfsWshKKeU2bPzky9Adnw8KXPm2BiRcrjSFMSgMolC2atBL7hnCmwZD4FV4donwS/I7qhUafEu+r+58SnzdbGqGNoTTqlKLuf8eQ7fPoysEycA8I2Npf4PU/AODbU5skrt6jdiGGNWWJZ1nTHmArIawhT8bFlW2FWHqcpWZiqc3AyRjSHEcxbmezLviAjqT5vKhfnzwRhCb+6Hd0iw3WEprnwT7rq8z/qrsjI4vhG+HQZpZ8HbDwa9C+3ugd2zYM8sqNoAuoyBgMpVF+FIyhE+3vox8anxDKg/gNsb3253SGXOOzSUiGHD7A5DFVLijRjGmPbAdcgIeIVlWZvdFpVyj4WvSvIFyMmE+X+A3GyY+ZTzmoNL4KG59sTnBlk5WYyaP4ozqbLSY93pdfh5+zG44WCbI1PuFLc9kY1z48jOyqXNjXXK7bbkEiVgY8yfgeHAj3mnvjTGTLYs63W3RaZKX8oJ1+P0ZNj0jeu5o6sg6SBkpcLqDyEnAzo9AnW7lV2cpWhrwtb85OvwU9xPAEzYO4FA70Aeaf0IXWK62BGecoPzZ1KZ+9F2cnPl/tair3YTUjWA2k3K37K7ko6A7wbaWZaVDmCM+SfSy00TcEXSchgs+7fzuOGN4FtotYOXD2Slwef9wdHKZ9cMGL0UoluWVaSlpmZITbyMV37DTQBvL29eXvFy/vHm+M3MGjqLmJDyOUpSv86x3Wfzk6/D0R1J5TIBl7TGYBxQsJ+HP9LZQlUkvf4AN78B19wE3Z+GYV9Ajxdc53yvfQqOrXUmX4DcLNg5tezjLQU1Q2oyts1YfIyMNa6JuIZwP9c57szcTFadXGVHeMoNqtUqeoOxas3yedPxSqsgPkDmfDOAncaYBXnHfYAV7g9PlSovL+j2mHw4BLaFp7bCtEfh0DLYMQWa31r0uaHRZRdnKXu0zaMMazyMxLREmlRpwuR9k/nxwI8u19QPL9o1QlVMNRtVoV2fWLYuPoaVa9G4czSNO9ewO6xi/eI6YGPMyF96smVZX5V6RJeh64DdaP1nMPtZ57Hxhvo94NASOa7dGe6fBn7lcxTxa2XmZPLishdZdHQRPsaHEc1G8EKnF+wOS5WyjLRsrByLgBBbNp1c/TrgkiZYY8wPlmV53tqeyuLoGtdjKwc6Pgh9/iqrJWp1AFM+e2r9Fn7efrx7w7vEp8bj5+VHRECE3SEpN/APLP/Vdksrwsrb4c8T1OkM2yc5j423JN3w2vbF5CbxqfG8u/Fd9p7by7U1r+Xxto/bHZLyYKWVgCvGfmZVvA4PQuI+WZIWVBVueq1SJl+AZ5c+y9aErQDsO7ePjJwMXu7y8hWepZR7lP8xunI/bx8Y8KZ8VGLn08/nJ1+HpceW0qt2Lz7d8SmZOZmMaDqCAQ0G2BSh8jSllYArzwShEtmZcPE0hNep8PO/uVYu+8/tJyooiqjAKBLSEvIfqxlck8cXP052bjYgGzeig6NpX6O9XeEqD1JavcZ/X0qvo8rKgUUw6X6Y8SQk7nd9bN9P8HZTeLcV/LczJB6wJ8ZScDTlKIOmDmLYzGHcPOVm+tbtS4S/3HSLDY2lQ40O+cnXYenxpTZEqjzRldYBb6f4+V1HNbTWyBc/uSE25S5xK2H87eT/p90zG57aAgFh0pxzxhOQmiSPJe6D+S/DPZMu+3Ll2fub3+fYhWMApOek8+OBH5l3+zzWn1pPQloCAT4BRZ7TIFzvKauyoQXZPdGOKbj8Xk1NgoOLoMVQSDsHFwu1KErYU6bhlSZH8nVIy07js+2f8fWurwEwGLrFdGPd6XXkWrn0rdeXgQ0G2hGq8kBXWgd8pKwCUWUotJiaBymn4IdHZBVEjRZwZqfzscY3l11spaxP3T7sStqVf9woohE/7P8h/9jC4uSlkyy5YwnZudlEBWmNZFV2SloNrSvwAdAM8AO8gUtakL2C6vQw7JoOZ3bIcYMbZJrBMSoOriHbkeN3S92I3n+yLdSr9VDLh/AyXiw5uoR64fUY3Xo0Q6cPdbkmKyeLRUcX8fOxn6kfXp9RrUYR5BvEWxveYtahWVQPqs7zHZ/n2prX2vQuVGVVopZExpgNwF3AZKAjcD9wjWVZf3RveE66FbmU5ebC8fUy77v6P7B5vOvjd0+AJv2LPsertO7b2ufN9W/mT0EA9Krdy+XGW/vq7elVpxdvb3w7/1yQTxALhy8k1E97E6gSufqtyAVZlnXAGONtWVYO8IUxRstHVWReXhCbVwM3uJg/u4OrO79OT4Hpj8vNuip1YeBbUsqygnq+4/O0jmrNzqSddInuwnub3nN5fFP8Jny9XOsHpGansitpl9YNVqWqpMOZVGOMH7DFGPNvY8zvgMpRmUVBl7HSjsih9Z1Qo7nUBQZY+gbsniE1Is4egskPSm+5CuLzHZ8zdPpQRs0fxZb4LRhj6BrTlXph9UjOSKZaYDWX6wO8A2gV1crlnJ+XH42rNC7LsJUHKOkI+D4kWT8B/A6oA9zmrqBUGQutAY+vgyMrpS39zh/hX/XAypX54uPrXa9PPw9JByCmtS3h/hpT90/lnY3v5B+PXTiW7wZ+x6j5o/I3ZNQPq08V/yqcyziHt/Hm6fZPM6zxME5ePMn8uPlUC6zG7zv9nioB5a+gt6rYSjoH/LRlWe9d6Zw76RxwKcrNgf0/QfJxmectWPfh4GL4xvUmFU0HSdNOh6Bq8Ltd4Ft0DW158+zSZ1lwZIHLuYENBjL70GyXc/+6/l9EBkYSGxZLdLCz9nF2bjY+XrpjX/1qJZoDLukURHF1gR8ocSiqfJn8AHx/F8x5Hv7TCU5scj52alvR6yMbQ5sR4BcC0a3gzvEVIvmCLDsryMt4Eepb/I20zjGdXZIvoMlXudWVdsLdDYwA6htjZhR4KAxIcmdgyk3i98h8rkNWqqyCuPUjOLwcQmqQt9HReU2jvtKUc+hHZR3tVbu/xf1sTdzKyhMrCfQJ5On2T3N9reuZdWgWF7MuAlA7pDa96vSyN1Dlka70630VcAqIBN4qcP4CUMxQSZV7OZlFz6WnyEj4fN6+m1odIfOS3HS79skK2xEZINg3mI9v+piktCSCfIMI9AkE4LuB3/HJ1k8I8g3iibZPEFS4OalSZaAkO+GOAN2MMTWATnkP7bYsK/vyz1TlVkxrqHsdHMlr6eflA/5hzuQLcGIDPLwIane0J0Y3KLjS4Xz6eZ5a/BRxKXEAHEk5wrg+4/D28rYpOuWpSjQHbIwZDqwDhgN3AGuNMcPcGZhyo3unwKB34Prn4ZEl4BdY9JpLiWUfVxmZsn9KfvIFWHd6HStOaI9ZVfZKeofhFaCTZVnxAMaYKGAhMMVdgalf6VwcLH5d1uk2HQjdnwEvbzl/ZifU6QrBeaNA30Do+JDzuW1GwJbvZNkZQFhtaNCr5N87Kw0WviYrK6o3g5v/DlXqlcrbcoeUzJQSnVPK3UqagL0cyTdPEqVXS1hdLcuC8cMgKa+u74mNYLzAPxTmvCCJ1ScQRkwoPrHW6w73z5AkHFQVuo795VUOlgWntkJwpCxhW/garM27QXf2oCT9sStL+U2WnsENBvPtrm/JzJX58GoB1ehZp6fNUSlPVNIEPNcYMx/4Pu/4TmCOe0JSv1rCXmfyddg9UwqpO0a12Wmw6G+XH9mei5PRc/p5uJRw+Z5wFxPg6yEQv1OS/LVPwgHXdbac2QEpJyGs5lW8KfdpVKUR4weM54f9PxDkE8RdTe8izE/rSqmyV9IEbAGfANcha5TGAV3dFZT6lcJiwDdIlpQ5RNSFk5tdr7uUADunwt65UK0RdH1URsm7Z0oRdoe4lfDMNggspl376g8k+YIk95XvSVJPKtA1Iziq+PoS5Uizas14pdordoehPFxJE3Afy7J+D/zoOGGMeQ1tRVQ+BIRD/3/B3N9LEo5sDDf9RRLkrmnO66o3l00YDnHLYORMKbJTUEYyxC2HZoPh0M8w90UZITcf4prkHVrfAZeS4Mx2SbxDPgRv36LXKaVcXGkjxljgMaCBMabgut9QoPxO8nmi9vdLgrxwWhKwMTD0Y6jZFk5vl+pl6z9zfc7hZXD+KFRtWPT1ql0jBXcm3S/TEgDbJsq25IJCakCL26DtPVLUPThSk69SJXSlEfB3wFzgDeClAucvWJZ11m1Rqd8mIFw+HHwD4brfOY93TXe93ttPGnJ6+0BMG7mx5uUjz6neTKYwHMnXITUJbv9MbtiFVIfrn5PvAzIVopQqsSttxEgGkoG7yyYc5VY9X4KjayAjb8lVvetgfIGidjf+SUay2ybCt3dAdGtJ6OnJzmvqdIZWw+Sjgtt7di+BPoHEhsXaHYryUFppxJPU7gDPbIe4FTLF8Hlf18c3fgkX42HdJ3K8fz7U7yFTC+ePyBRHz4o/7Z+alcrYhWPZFC9FiG5peAuvd38dY0pUwEqpUqMJ2NMERkCzQbKWN6fQbvKcTNheqP183Ep4JR5SToBfsHxUcFMPTM1PvgAzDs7gloa3aLcLVeZ0M4UnysmSqYg2d7me7zoWQgut3Q2pDt8Nh/daw1tN4KeK26DT4eTFkyU6p5S76QjY05yLg68Gy+oHjHQ/rlIPfAJkB11YTTh3WJabeftD/V6wLW//TW42rHofWgyFWu3tew9XqU/dPozfPZ7cvE1kBiedAAAgAElEQVQqQT5BXF/7epujUp5IE3BldXg5rP1Ydqt1e8LZgHP5W3nJF8CS2sBDP4EfH3E+NygShn8pZSkX/63oaycdrBAJ+MTFE+w9u5d21du5tBNqW70tH9z4ARP3TiTQJ5AHWzxIZGCkjZG6n5WbS+q69eBlCOrYEVMJultXBpqAK6P43dJWKDdLjvf/BI+tgar1ZYtwQVYubC9UUyk1UdoW+QbKTbiNXzgf8w3+dYV6bDJp7yT+vvbv5Fq5BHgH8N6N73FtzWvzH+9Ruwc9avcA4FDyIeYdnkeHGh2ICirfO/h+i9y0NI6MfID0bbKUP7BdO2K//AIvf3+bI1P6a7Ay2j3TmXwBstOdu91aDXe9tto1UKNF0deIWw5vXgNTHoToNlC7E1zTB+6bCiHlO0ll5Wbx7qZ386cY0nPSeX/T+xw8f5DHFj7G4KmDeWfjO2TlZvH1zq8ZMm0ILyx7gX4/9GPlicq3vyhlzpz85AuQtnkzF+bNszEi5aAj4MqouEI6lxLg4+tlbrfVHZBxAUKjISIWUs9Clfoy9wsyx7vmI/LbEp3eKlubHZs6ko9Lq/qkvNKXXR+TVRJzfw+ntsioud8bEGhPF+GsnCwuZV1yOXc2/SyPLXyMk5fkL4DPd3yOr/Hlm93f5F+TmZvJf7f8l+61updpvO6Wfbbonqnss+dsiEQV5tYEbIypA3wNRAO5wDjLst4zxlQFJgL1gDjgDsuy9F9EaWl5u0wrHFwkxw1vgFUfSIshkMI5d30HK951Ti94+cKgd6Fud0jcK0V7CjqzSz47Sl8m7Jbjo6tkGmP3TDi+Ts5t/V5u2N3+qXvf52UE+QbRt25f5sU5R3k9avdg4t6JLtetOLGC9Jx0l3PJGclUNmH9+5P40cdYqVLHwys4mLB+N9sclQL3j4Czgecsy9pkjAkFNhpjFiAdlRdZlvVPY8xLyDbnir/Cv7zw8Yd7psjWYr8gOL4eDi5xvWbrRGfCBJmyOLIKOj4o9Rx8g6HgKLJRH/mcuM+ZfB12/AinClVeO7i49N7Pb/D6da/TvFpzdp/dTbeYbvSt25eZB2eSmu0sJtSkWhNiw2OZe3hu/rnbG99uR7hu5Ve7NvW+/45z330PXoaqI0bgG6PbxssDtyZgy7JOIU09sSzrgjFmN1ALGAL0yrvsK2ApmoBLz85pMh1wKR6aDIBujxe9JqoRFMqj5GbJzbvDy6FaQ1malpUG7e+TimcAwdWluHt2mvN5VRvI9uazB53noluV+tv6Nfy9/Xmw5YMu517r/hqvr3md5Ixk6ofX59TFU4T6hTKy+UiSM5PpFtONAQ0G2BSxewU0aULMa3+xOwxViLEs68pXlcY3MqYesAxoCRy1LCuiwGPnLMsqMmFojBkNjAaIjY3tcOTIkcKXqMLSzsFbzVwT5HW/k7q/y/4PsjNkA8bg9+H7u5zF1P1CpGHnkVXO50W3hkeXy9ebvoFFr0kH5dod4fgGyMmQueP7fpQecj88LFuWa7SE4V9B5DVl975LKDMnk1UnV/H0kqdd1gHPvm12pV+KpspUifa1l8lNOGNMCPAD8IxlWSkl3XNvWdY4pPg7HTt2LJvfFBVdwl7X5AtwYhNENpIdcL5BUhfY2weuexbSzsv11z8Ps3/n+rzT22QEnHISZjxJ/k25Iyuh79/lZluNFtJ7rmoDeGqLVE8Lqlomb/W38PP2Y/3p9fnJFyA1O5Xlx5cztNFQGyNTnsjty9CMMb5I8v3WsixHQfczxpiYvMdjgPjLPV/9StGtIKBQJ4vACFj/qUwxZF6An/4oXTG+uRVOrJcWQj8+IiUoXV6rtawFPrmZ/OTrkLBbRswFW7l7eZXr5OsQE1x0/jMmROdEVdlzawI2MtT9DNhtWdbbBR6aAYzM+3okML3wc9Vv5BcMd38PNdvLfG2XsVDc5oJtk2QKwSE3C2LayyYL4y274Do8CPNehotn5FxBdSvuUq3bGt1G++rOnXyDGgyiS7QW4lFlz61zwMaY64DlwHZkGRrAy8BaYBIQCxwFhl+pwHvHjh2tDRs2uC3WSm3XDJh0n/PYeEO/f8LcF1yvu/UjaDtCvl77ibQicqjfE5KPSW3g9iOh95+l60YFtvfsXgJ8AqgbVtfuUFTlU6L/OcrsJtzV0gR8lVa8A+s+lWVpvf4AzW6BySNhzyx5vEYrqXwWHClzwxNGuK5q8PKFl47K85VSV6IJWJVAwl6p+VvwBlxQNUnG8QXWqfkGw+8PyxpjpdSVlCgBay0ITxfVRDZqFJSaBI36us77Xve7Sp98T1w8QXq2c2fc2lNr+cuqv/Dx1o8r5Q45ZT+tBVFRbP5WtvgGR0HPF4uuWChO0kFY+a7Uemh3LzTpL1XO9sySusBNBsjytIg6RZ/bchi0u086J8e0kbW/5+KkbZFlQYeRsvSsgph1aBbT9k8jzD+MMa3H0KRqk/zHTlw8wROLnuDA+QOE+oXyardXCfAO4MnFT2Llrf5YfHQxEwdN1LZFqlTpFERFsHMqTH7AeRwUKb3dfmk+NvMSvN9OVjA43PsDbB7vrPPg5SubKGq0hK+HyLpfDHQeDQP+LfUfstKk9u/FM/BhV9noAdKs87E1UsC9nFtydAlPLXkq/zjML4z5t88nxC8EgBd+fsGlbkSoXyjtqrdj2fFlLq/z/cDvaRnZsmyCVhVd+dmIoa5S4XbyqYkw/XHo8qiz0Hphh5e5Jl+ADV84b7qBLD1b/V8YMRHGLJPaEYFVpELapJGwa5pcV6sDNBvsTL4gqyG2T4HuTxV4vVyIWwaZqXBNb5myyLwEWyfAhdNSZa1G89/+c/iNFh5d6HKckpnC2tNr6R3bG4CDyQddHr+QeQE/L78irxPqF+q+IJVH0gRcEVSpV/Tczh9lJDvsc6jZFmY/Jwm0fk8Y+BaEFrOxIKR60XM52bD8bTi5CepdD50elkI6juQL0qooopilWv4hzq9zc2Rjx+G8UWPVBvDQTzDxHji2Vs6teAcemAWxXUv81ktD7ZCi5Tl3J+3mwy0fEuATQP2w+uw/tz//sYbhDXm87eNsOLOB8xnnAbi90e26XE2VOk3AFUG3J+HQ0rwdaQVZ0nYoO12SL0hiNl4w7DPZSOEoNxnVFHq9LG3nHaNgLx/phLzoNTnePVPW+kY1LRpDcJRMVZzZkfd6zaS4e2aqTIUcWOhMvgBnD8GSfziTL8iIe/2nZZ6A72l+DytOrmBbwja8jBe9Y3vzybZP8h/faXZyZ+M72Ri/kQbhDXimwzPUCa3D3NvmsurkKqKDo2kd1bpMY1aeQRNwRRBcDUYvlUI5XwzEuacFwDiTr8PhZZIQzx7Kmz64Ba59SrYKD/9SqqWdi5MSk/+70fW5WyfA2NXgHw6OO//GGzIvyrxvi6HQ9BYZdY+/XRJs1YZFO22AFH8vzMv3t/4UfrMwvzC+HfAtB88fJNQvlC92fOHyeI6VQ7NqzXil2ysu50P8Quhbr29Zhqo8jCbgiqTutdDpIRlFgoxgezwP816SIusOkY3guzulKDrIyLlBLxn9rnhHRr3dHpNaD0HVpGylQ3AUbPhcVllkp8l8cFoybPnWeY1fCOz8wTm6PXtQHg+p4Zx39vaX+eG0s9KTzvG8rmPd8ZMpkYYRDQG4JqJolbZrqpS/ym2q8tNVEBWNZcGhJZCwT0aw1RrKHO2PYyBpv/Ruq99Duh8X1Hm0JFZHUsbAQ/OlfOS0x2R6wCcQ6nRynUroOAo2fVXgeUgiDYiAlOOu32PMctgxRaYl2t8vxXpysmHfPLhwStoXlYNVE1m5Wfxp5Z+Ye3guPsaHB1o+wJPtnrQ7LFW56E44j5N5SYrx7JkDE+52faz1XbBtguu57r8DKxu2/yAV0/q+Dt/d4Zps/cMhqIpMWThUby5rg7d+X+BcC3isQC3hCiA5IxkfLx+CfYPtDkVVProTrtLKzYWl/4QPOsJXt0i9X5DkC9C4H7S9h/x/Ay1ug1bDir7OucPSK+7CSYjfBVPHyDRCQWE1of+bMuoFScj93pCPVsNlCqNBL7jjaze8UfcK9w/X5KtspSPgimjNRzLv6xBUDZ7eDiveljKTodHQ5zXpVpF2Xvq1+YXA4Z9lLbCVK8kz5SQcWeH62je9Jh2Ps9Ol/sOd38ia3vQUSNgjo9+Cy8+UUsXRjRiV1gHXjQWkJsHiv8Haj+Q4+ajchHt4IXwzxHljrE5XeG6vfB0SBfP+4JqAfYOgwwMyf3tmh0wrnNoMR9fI0rE6nZ3Xpp2DbZMlUbcaDmExcDEB1o2DSwnQ+k6o281tP4KrNXnfZL7f8z2B3oGMaTOGHrV72B2S8kA6Aq6IfvoTrHrfeeztB3Wvg0OFOhG3HA47Jrueu3uibDneOU2mFzJSZDVDYBWo1wPqdYc2d8s88Gd95cYeSHGeuyfKUraMi/DxdTKFATICf2SxtKt3XG+84P7pckOwnFlxYgVjFzpXY/h4+TDz1pnUDi26YUOp30jngCut65+VHW8gc7ID35JiOQV5+TrnhAvaORWW/B3id0pDzuQTsjstMw12T5ci7F/0l6VuSc7dYez/SVZfAOyZ7Uy+ICPwn//ter2VK408y6GVJ1a6HGfnZrP21NrLXK2U++gUREVgWfDzv2DLd7JO96ZXocsYCKslqxFa3yVre09vlyVfjhUNtTrA9knODRFV6sO5Qp2lU47D2nGQ4yzDyJkdcm1hqXlNS0wxv7d9A4qeCyzQm27HD1JJLSAcrn8Oarb7VT+C0tSoSqMSnVPK3TQBVwQbv5QbYyDrdsffLgkXYOt3cGIDDB0HDXrKlEJUM2h0s8zzDv9KSlIGR8HN/4Dl/wfHVjtf2yeg+Eaa1/SWZJ6bJcfBUdA4b1dY04Gy2SMxb8QbXB2ufwFSz8lWaJDVFN0el68PLIQpDzlf+9DP8PRW2xp4Dm44mPWn1zPn8Jz8dcC61VjZQRNwReD409/BkXwddvwgI9Zl/5bjIyvl5lm/f8LEe53NN1OT4PbPZOvyiY3gFyrLyepeK3PC6VJ4hrrd5WZcdCvY+JWMbrs9LqPXffNh9vNSMyKmrdyAa3OXtDIa/oWMzC/GQ8Mbnaslds90jTcjRQr+FLc0rgz4evnyxvVv8FLnl/D18iXIV9ssKXtoAq4IolsVLUlZkLe/zMsWdGKjVDkr2Pk4brms+b1/OpzZDdEtnPPET6yXRBkYAU0HS8PN5GOwf74k1OQTMtc8ZZS0tgc4tUVG3cGR0r7oxCZZLRHbFXKyYNmbMtrNzSkac9VipjjKWLh/uN0hKA+nCbgi6Pq4jFp3z4KAMJnz3fiFcyTc4zlJfvE7nc/xDZbphcJ2Tod1n8i8cJ2u0sI+qCocWycJ+8IpKbjT568wdazUgwDYO1tGtI7k63B8I6z7H8x5Xo6NF9z2P/kFsOZD53WBVWTpmvGCzmNkflopD6cJuCLwC4I7x8tmCJ8AWW3QZgQk7JYC5zFtJBGe2AQXT8uytJv/LrUY9s1zJtG618HqD5xbjY+tkZoRPZ6HHx52XrdjiryG49gh5aTUgHBMVYBMXyz5u/PYypUylJmXXJ+bdh5G/yw1ictBPQilygNdhlaR5GTCp73ho2vh0xtlKViNljDjSfi8ryS59vfDs7tlpDz9SbkZ1uwWKdze80XXOg8Aifuk9VCRZHtCkm1BDW+Eu76VKZGAcJkn7v60FN8pKPNi0T5zIdWl+pomX6XyaQKuSFa9n9e3DbByZO3tqv/Apq8lseaky9cHFsuINn4nnI+D3TNks0SdLrKaoaDYrtI6yD/M9Xz9HnD3BJkqCI6C2G5SLa1GC3h0hayuOBcnxXsKb7bo9DD0/TsE5q1y8A2C/v+WTRxKqXw6BVGRFKxIBoAlS9AK2ztHpgIKOrRUiubcNxUWvy4jXL9QWPQ34K8Q2URGpxdOSUfka58CHz9JtJ/0gKOr5WP9OLjjG/h2uHOJGkCPF2SdcL3roOVtcu7ZXXB6hyxZCyw0mlZK6Qi4Qml2i+txcHW5IVeQ8Zb284VFt3J+HjERhvwXjq6CvLbrJO6VxDvoHVkt8X47Kd6+5Tspqu5w9lDePHKW6+tbuTDobUm+liUj8R8fkVUU2spdqWLpCLgiaTVMit9snSBzu+3vg0uJ0ONF2WLsGyBfN79FOmSsyrvhVrOdrKDIuChzxMYUM5pGpjeW/sM5el74F0nKhYUU0/CzWoGOEqvehwV/lq93z4Tj62Xpm1LKhSbgiqbdvfKxZ44Uv8nNAgwMeBM6PwJZ6XByi9wcu/45qfG7axocXye71FJOwg1/kFoSAeHSXt4hNKbo1IVPgIy0HW2LIpvIdEP6ORnlWrkyB3zoZ4hbKRsxthYq/H5oqbSlD412509GqQpHE3BFtfj1AtMAlpSjrN4cJt0nO978w2DIh3IDrqDN46HTKNg7F254RXbNpZ2TkXFkY1kjXFC966S28M6pkoz9QqTVvI+/zAUHRMC3t8vIHOS6mFaur+EbDP6hbvkxKFWRaQKuqAqOXEHW3c57SZIvyHbfeX+QRFxw3a5fMHzQQR4HaDIQRs6Ao2ulZ1z9nnByE2RnyJzziU1S5KfTKFnm9tlNzlHyoaUy6s4uUMgn6xLEtJedcenJsvGi95+Lr8ymlIfTBFxRdXgAlrzuPG59F+yZ5XrNhRMyNTHnBUmaPoFSQS1xr/OavbNh83cw4wlZ2gYQHis75D7rKwkVpOtx6ztcpyiy02VqobA6HeHGl6XOcGSTomuClVKAJuCKIydbbmZ5+8pNtZ4vQJW60mYopq0kZC9v6WDsENsN9v0kmzXqdIZef5B6v4XtnuFMviAdNVZ94Ey+IKPotHNFn9tiKJw/6mxRX7+H1JLw8YNrbnJel7hf5olju0J0y6v6UShVWWgCrggunIbPb3auXPANkiaYLYZKDYdFf5MC6r1flU0TcStkw8S2Sc7aDae3yfRCx1FS2MexI65me2ltX1hwZNFzDW6U7dD75wNGqqB5+Ul35YAwmReOaSuPp52DJgOkzsTMZ6R2hUOzW6TXnFIeTlsSVQTz/wir/+N6zj9MVjos/pvznG+QbEMOjJDlXxPvdX1O67ug40Oytjc1UW6wtbtX5mo/6yubMwCaDoKhH8NnNzsL/EQ1hRqtZKlbs8FQtaGs8z2Z15G5enN4cB5MuhcOL5NzQdXgtnFSv7iwh+bLaFipykmbclYaKSeLnstIkZtgBWWlSonI6NaypKywzEtSM8IhrKasTvAPhSc2SI3eoKoyOp7+uIyiO4yUke3s56UrMkjt4Jv+7Ey+IG3tl77hTL4gNwQ3FpgSKejCqRK9daUqM03AFUFxnSPC60oTzbjlznM+AdJeaO+cvEad18Kx9bJcrd71UninoA1fyLRF8nEZBTcZAKe3wv96O+eE986VJWoFd75lXoAjq4rGVHC1hUNglaLrjQFqdyrZe1eqEtMEXBEUNwJu2h8a9ICzB6UjRkgNuem16Ut5PCdDkuR9U6FqA6hSDz7q7voaxkvmZx2dk2u0lG4YBW/IZV6AS2eKfv+GN8kI3HFjzi9ENn6c3OwcKXv7y5RH5iUpcVnQsXUQrl2IlWfTBFwRnD9a9Nzaj+Wj+RB4JV4qjc15oeh1545Kkty/QJLh7OfIr//QbLBr2/ozO4ovF9n8Vjh7WIqsgyT6mNYw6D1ZmWHlyJbl/T9B/euh4Q2y5C26Ncz9vetUhYO2AVJKE3CFULAYTmG7pkuVsnrd4Zo+sG6c8zFvP9kufDIvcQaES2H3swdltJuRUnRkGlwdaneWrcsgUxc128PDi2SpmbefLFH75Hp5PKYN3D8DvrlVRr8AXj5w748w7THpulyczIu//uegVCWj1dAqgsimv/y4o05D475SzaxGS6n9e8MfnckXZB42brnU+N01HU5sdtbsBcBA2xHw8AK481u5kRe3HN5tJZXRYrtCxgVn52OQVkmL/+ZMviA38Vb/5/LJFyCqSYnfvlKVlY6AK4Jb3pf524zkoo8FR7lueMjNkbldH38ZiRZ2Lg6+HET+NEREfWkzf2andNxY+7Gsitgz27lSwcqRtkNt7yl+9UJaMXEFVZMPx9ZoB29/uO53zvKYSnkwTcAVQUQdeH6fjEaDo2QUuvkbaUl0MV6Ko9/wR5mqcDTHBFkBEdVMeseBzMvmZJKffAHOH4Yq98rrgcwDH14m63oLys2WfnON+rr2hTPeUgEtO825Fdo/XNYot74DZj4tc9gNb4S+b0B4jEyFKKU0AVcYvgHQqI/zOOWElJoEZ2ugpgNdn5OdBt2fci4z6/hQ0Q0dAMcLbXBJPw/Vm+UVbM8T2VgS5/w/QkQseDWQzx0fhDqdoN8/IboNBIZDy9tlJ131ZvD0Nins41tMh2alPJwm4IrqwCLX46zU4qcctk6Ew0sBI6shrntOirM75o1b3ym72vbNdX1ep1FQs61suqhSV5aYfTPUdS1xi6HS5mjzeJjxlExV+AZJsm54g1xjjCZfpS5Db8JVVDUKTRFgoPMY5yjZ219qLhxemve4JRXNzh6QBpk1O0Cjm6W4epcxsvIBZEqh+zOy9fhcnCTc09vh8PKiGzl2z5QiQT/9ybl2OCtVOmkopa5IR8AVVecxMnWwZ7bU2o1uKduMvf3gumflRteqD4oWZN83T+r+OpzYCE9vhb6vw9oPIbgGdB0L6/4Hy9+Sa5KPQfweubGXneF8btX6MqdceAfcpQT3vGelKhkdAVdUfkFw17fw4iEZ0R5dI8kw8yKseFtGq036y4oIBy9f6SFXUGqirB3+or9MN6z7BD7rI3UhCsq8AB0elO3OILvY0pNh+mOyVrigNneX/vtVqhLSEXBFF1RVCuEUdmqLLE8b8Kas+fXykVbzR1cXHRU7drM5nD/qnJJw8PKVkXWvl2TkPeFeSJ7nfKzrY3Kzr0FP6PBQ6b5HpSopTcCVQf0erqsbjBfsX5i37RhJxHd9J1MIVepJk86EvK4Y7UcWvyys/f2QlSaFfQKryHWbv5Ei7wl7IadAG6LcLNnQ0e+Ny8eYmQoHFsr3qt9DW9UrhSbgyqHxzXDzG7KJwjcQGveDle86Hz+wUDoVJ+yV6YbcbLkB1/dvsiMt+YQU9HHUA67ZHrZNlGpqg9+XLhuL/+p8veKmGIqrIeGQckqmNZKPyXHD3nDvD5qElcfTBFxRHN8gHS6Co2SJWFBVqSiWkyWj0m6PyQfIDbTC4pbB9gJ1H/bPl40SkY1l3vjhRVI059BSSdKOAjrH1rrW+AUZXTfqK8V3QEa0rYa5XnNktXyPyMaQeMCZfAEOLpLXbNDzqn4kSlV0moArgriV8NVg5zzt9kkQEi1JFWTE+sAsZ+fhRn3lZpmjW7HxkusLO7YOlr8tXS98AqDP36RvW0G7Z0q7IRcW3DMZTu+QmGLa5JWlNNILbvsU+GGU8/KqxbQ8KlwfWCkP5NYEbIz5HBgExFuW1TLvXFVgIlAPiAPusCyrmG6P9snMzuW1mTuZseUk0eEB/GlQc3o0jrIvoE1fu94kS9znuib35CaZYsi4AKv/KzfcOj0sJSRzMmWdb0QsrPmoaPNNR8uh7HT46Y9FazT4BkLXx2HpP5zn2o6QhBvdUnrEfTtcRsMBEXDz32Hjl66vcfagNBPNySvqHl7HtX6FUh7K3cvQvgT6FTr3ErDIsqxGwKK843Llf8sP8e3ao1zIyGZ//EUeHb+RlPSsKz/RXfxDr3zN0bWw8FXZ4XbhpNyU6/EcjJgEIdXlJtmd30CtjjJiHfoJZBQqCZmTCW1GOJeagax66PV7uH+6rKKoUg9WvQ//1wTWfCxV0hxTEennpcB74XZYxhtGTIFOj8D1z8OoBbKMTikP59YRsGVZy4wx9QqdHgL0yvv6K2Ap8Ht3xvFrrTvsWn83NTOHnSdS6Nawmj0BdR0LO6fKml2Qur/H1ko9X5Bdb36BRZ+3dy78OAaS9stSsZtehUfytjBfOC1rggu2NIqIlfb2zW+BIytlU0fcSlj8urObsqMzc04G/PSK3KgrKDcLmg6Q8pSONkadRkHDXvKhlMpnxxxwDcuyTgFYlnXKGFP9chcaY0YDowFiY2PLKDxoFxvBz/ucu7kCfL1oHlN4HrQMVWsIT22WkWZIddn4kLBHVj3kZEOnh6QqWuE//U9vl+QLkgwX/kVaCc16Bo6tAb8w6WRx/oiUsczJhAl3Q8+XILIJ/O8G5zzylu9k51tBuVlSe/hwgXnjgAjoPFq2QR9YBFGNpV6EUqqIcn0TzrKsccA4kLb0ZfV9H+3ZkCNJqczadpLqoQG8Org54UG+ZfXtixcQ5rrSoHozGPye6zU9fw+rPwQvb+jxvLQhKig3G35+Q5IvQGaKbGUe9DZMGyvnzuyQXXWt73QmX5Alag16ASuc58Jj4aa/yHTC9skQVivvOFh+aVQr5uabUiqfHQn4jDEmJm/0GwPE2xDDLwrw9eadO9vy9h1tMBVpreoNL0OvPzjX1/oGuY5Oq9SHi4XqNGSnwY6prucyUopvg9RsiIx4d/4o0xW9XpZVDze+Ih9KqV/FjgQ8AxgJ/DPv83QbYiiRCpV8HQrG3GkUYEmNh4hY6PmizCUXrPMbWhNqd4ADP7m+TsdRUtA9Oa8haN3roNFN4HWzc72xUuqqGMty31/2xpjvkRtukcAZ4FVgGjAJiAWOAsMty/qFrpOiY8eO1oYNG650mbqS3Bz4+V+SiCPqQp/XJDlPGCGbI7x8pfbvDX+Q7cP7fwL/EGhwg0xtAKSelZUZ3jZPyyhVfpVo9ObWBFyaNAGXgXNHJLEGVS3+8UuJMGkkHFkBQZEyd9x8SNnGqFTFUKIEXK5vwtklKyeXf8zZnb8R448Dm3Ftw0i7wyo9p7dLrYegalJkx5Fwq9R1ve5cnNSICAiXm3JL/i7JF2RJ3LTHpNdbSdYpK6WK0ARcjM9WHOaLlUHTNU8AAAi5SURBVHEAJF3KZMzXG1n9cm9C/CvBj+v4RviiX15zTmQH3aMr4ewhWckQVFV2uqWcgk97S50IgA1fyK64gjIvSpLWDsdK/SaVIKOUvjWHXFupX8jIZvvxZPs2YpSmTV86ky/IeuKNX8o2ZMeys03fQL3rnMkXZHlay+FSO9ghrJZ0XVZK/SbaEaMYrWu51sf19/GiaXQl+TPbN7jouYOLXdf8xu+ElJNFr2t1O3R7Qm7aNegFIyaCt/4OV+q30v97ivFor4YcTLjE3B2niAzx59XBLagS7Gd3WKWjy2ipppaaN8pv2Bsi6hS9rvlg2abs6PdWq4NUWWvSXwruKKWumq6C+AUZ2Tn4eXtVzPXAvyTtHOydJzfhrukt87if9s4rKYms+X1gFlw8I/UfAiJktUNOphT8iVsBNdtJI8+Qy+4kV8qT6SqIq+Xv4213CO5hvKWOw8XTkHlJtgw/sQH2zJKqaU36S7Ld+KW0o6/ZFpr0gzkvwrYJ8hqJ+yRB319u99EoVe5pAvY0aedhXE9nVbNl/wdjlkFwpFRCc5jzgnTGAFl6dvaw6w46kO4Z2RnSa04p9atpAi5Gbq7Fu4v2M2PLCaLDA3ipfzPa1omwO6zSsWOKM/mCVELbMUVGwpu+kWVoN74iO+UK2jsH6nSWMpgOVRto8lXqKugqiGJ8tTqO9xftJy4plTWHzvLAF+tIy8y54vMqhNxi3sex9bDgz1K68tha+PaOok02Q2Ng4FtSkB2kxdEtH7g9XKUqM03AxShYCxjgfGoWW4+ftymaUtbydtf+cCHRUhGtoOw0qecbmLdDzjcYBrwpGy6e3AzPbIff7ZS1wkqp30ynIIrRNDqMpXudSdjX29AwKsTGiEpRcCQ8ulx2wAG0uQu2fi+rHfIZaDEUuj0OZ3ZBZCNnY04vL1kHrJS6apqAi/HYDQ3ZeTKZ5fsTCQvw4ZWBzYkKrURznSHVoftTzuPOo2WH2+68zso3vOwspl67gz0xKuUBdB3wLzh3KZMgf+/KuxytsLRz0pCzcM0HpdSvpeuAr1al2f1WUoFV7I5AKY+iN+GUUsommoCVUsomOgWhxJbvYPN4mYbo+SLEtLE7IqUqPU3ASlrTO9rSg1RBe2a7dMJQSrmNTkEo2D3T9Tg9WYrwKKXcShOwkpoOJTmnlCpVmoAVdBkDdbrK18Ybrvsd1Ghub0xKeQCdA1Yy1ztqPiTul6+1yLpSZUITsHKKbGR3BEp5FJ2CUEopm2gCVkopm2gCVkopm2gCVkopm2gCVkopm2gCVkopm2gCVkopm2gCVkopm2gCVkopm+hOuMvYcSKZ2dtPERMewLAOtQny0x+VUqp0aVYpxuqDSdz32Vqyc6Vh6YwtJ5ky9lqbo1JKVTY6BVGM8WuO5CdfgA1HzrHt+HkbI1JKVUaagIvh51P0x+IxremVUmVGE3AxRl1Xn2A/Z8Lt1yKaJtGhNkaklKqMdA64GC1rhbPk+V4s2H2GmPAAejXW+rhKqdKnCfgyqocFcE+XunaHoZSqxHQKQimlbKIJWCmlbKIJWCmlbKIJWCmlbKIJWCmlbKIJWCmlbKIJWCmlbKIJWCmlbKIJWCmlbKIJWCmlbGIsy7ryVeWAMSYBOGLDt44EEm34vnbR91u56fstG4mWZfW70kUVJgHbxRizwbKsjnbHUVb0/VZu+n7LF52CUEopm2gCVkopm2gCvrJxdgdQxvT9Vm76fssRnQNWSimb6AhYKaVsoglYKaVsogn4Mowxnxtj4o0xO+yOxd2MMXWMMUuMMbuNMTuNMU/bHVNZMMZ4G2M2G2Nm2R1LWTDGxBljthtjthhjNtgdj7sZYyKMMVOMMXvy/m13szumwjQBX96XwBUXUlcS2cBzlmU1A7oCjxtjmtscU1l4GthtdxBl7AbLstqW57Wxpeg9YJ5lWU2BNpTD/9aagC/DsqxlwFm74ygLlmWdsixrU97XF5B/qLXsjcq9jDG1gYHAp3bHokqfMSYM6AF8BmBZVqZlWeftjaooTcDKhTGmHtAOWGtvJG73LvAikGt3IGXIAn4yxmw0xoy2Oxg3awAkAF/kTTN9aowJtjuowjQBq3zGmBDgB+AZy7JS7I7HXYwxg4B4y7I22h1LGetuWVZ7oD8yzdTD7oDcyAdoD3xkWVY74BLwkr0hFaUJWAFgjPFFku+3lmX9aHc8btYduMUYEwdMAG40xoy3NyT3syzrZN7neGAq0NneiNzqOHDcsizHX3JTkIRcrmgCVhhjDDJXttuyrP9v735eZYrjMI6/n7CSLCSRYncXRm6NneUtaRZW148FpawsbJU/AMlNIpZWZKnklh8lopSVwVXXxoIuf4G7UB6LOZcp7Mx8OPO8amo6M1PPWczTt9P5fs7F6jyjZvu07a22twOHgUe2jxTHGilJayWtW3kP7AVae4eP7c/AB0lTzaEZ4G1hpN9KAf+BpFvAc2BK0kdJx6szjdAe4CiDleDL5tWrDhV/1SbgmaQ+8AKYt32vONOonQRuSnoFTANni/P8IluRIyKKZAUcEVEkBRwRUSQFHBFRJAUcEVEkBRwRUSQFHBOl2ZI6CYOG4j+Q29AiIopkBRyt1ez+mpfUl/RG0iFJjyXtlrR/aNPJoqT3zW+6kp40A2vuS9pcfR7RXingaLN9wJLtXbY7wI+dX7bvNHNxp4E+MNfMw7gCzNruAteBMxXBYzKsrg4QMUKvGRTreeCu7aeDsRc/SToFLNu+KqkDdICHzfdWAZ/GnDkmSAo4Wsv2O0ldoAeck/Rg+HNJM8ABBoO7AQQs2P7nHl0T7ZRLENFakrYAX2zfAOYYGkcoaRtwDThoe7k5vAhsXHl2mKQ1knaMOXZMkKyAo812AhckfQO+AicYFDHAMWADcLu53LBkuydpFrgsaT2D/8clYGHcwWMy5Da0iIgiuQQREVEkBRwRUSQFHBFRJAUcEVEkBRwRUSQFHBFRJAUcEVHkO8JfZqnz5rtUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"size\", y=\"total_bill\", kind=\"swarm\",\n",
    "            data=tips.query(\"size != 3\"));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other option for chosing a default ordering is to take the levels of the category as they appear in the dataset. The ordering can also be controlled on a plot-specific basis using the `order` parameter. This can be important when drawing multiple categorical plots in the same figure, which we’ll see more of below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"smoker\", y=\"tip\", order=[\"No\", \"Yes\"], data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We’ve referred to the idea of “categorical axis”. In these examples, that’s always corresponded to the horizontal axis. But it’s often helpful to put the categorical variable on the vertical axis (particularly when the category names are relatively long or there are many categories). To do this, swap the assignment of variables to axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 429.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"total_bill\", y=\"day\", hue=\"time\", kind=\"swarm\", data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distributions of observations within categories\n",
    "\n",
    "As the size of the dataset grows,, categorical scatter plots become limited in the information they can provide about the distribution of values within each category. When this happens, there are several approaches for summarizing the distributional information in ways that facilitate easy comparisons across the category levels.\n",
    "\n",
    "#### Boxplots\n",
    "\n",
    "The first is the familiar `boxplot()`. This kind of plot shows the three quartile values of the distribution along with extreme values. The “whiskers” extend to points that lie within 1.5 IQRs of the lower and upper quartile, and then observations that fall outside this range are displayed independently. This means that each value in the boxplot corresponds to an actual observation in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"day\", y=\"total_bill\", kind=\"box\", data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When adding a `hue` semantic, the box for each level of the semantic variable is moved along the categorical axis so they don’t overlap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 413.359x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"day\", y=\"total_bill\", hue=\"smoker\", kind=\"box\", data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This behavior is called “dodging” and is turned on by default because it is assumed that the semantic variable is nested within the main categorical variable. If that’s not the case, you can disable the dodging:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 422.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips[\"weekend\"] = tips[\"day\"].isin([\"Sat\", \"Sun\"])\n",
    "sns.catplot(x=\"day\", y=\"total_bill\", hue=\"weekend\",\n",
    "            kind=\"box\", dodge=False, data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A related function, `boxenplot()`, draws a plot that is similar to a box plot but optimized for showing more information about the shape of the distribution. It is best suited for larger datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds = sns.load_dataset(\"diamonds\")\n",
    "diamonds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"color\", y=\"price\", kind=\"boxen\",\n",
    "            data=diamonds.sort_values(\"color\"));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Violinplots\n",
    "\n",
    "A different approach is a `violinplot()`, which combines a boxplot with the kernel density estimation procedure described in the distributions tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/luke.thompson/miniconda3/envs/python3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 429.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"total_bill\", y=\"day\", hue=\"time\",\n",
    "            kind=\"violin\", data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This approach uses the kernel density estimate to provide a richer description of the distribution of values. Additionally, the quartile and whikser values from the boxplot are shown inside the violin. The downside is that, because the violinplot uses a KDE, there are some other parameters that may need tweaking, adding some complexity relative to the straightforward boxplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 429.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"total_bill\", y=\"day\", hue=\"time\",\n",
    "            kind=\"violin\", bw=.15, cut=0,\n",
    "            data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also possible to \"split\" the violins when the hue parameter has only two levels, which can allow for a more efficient use of space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"day\", y=\"total_bill\", hue=\"sex\",\n",
    "            kind=\"violin\", split=True, data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, there are several options for the plot that is drawn on the interior of the violins, including ways to show each individual observation instead of the summary boxplot values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"day\", y=\"total_bill\", hue=\"sex\",\n",
    "            kind=\"violin\", inner=\"stick\", split=True,\n",
    "            palette=\"pastel\", data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can also be useful to combine `swarmplot()` or `striplot()` with a box plot or violin plot to show each observation along with a summary of the distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x=\"day\", y=\"total_bill\", kind=\"violin\", inner=None, data=tips)\n",
    "sns.swarmplot(x=\"day\", y=\"total_bill\", color=\"k\", size=3, data=tips, ax=g.ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical estimation within categories\n",
    "\n",
    "For other applications, rather than showing the distribution within each category, you might want to show an estimate of the central tendency of the values. Seaborn has two main ways to show this information. Importantly, the basic API for these functions is identical to that for the ones discussed above.\n",
    "\n",
    "#### Bar plots\n",
    "\n",
    "A familiar style of plot that accomplishes this goal is a bar plot. In seaborn, the `barplot()` function operates on a full dataset and applies a function to obtain the estimate (taking the mean by default). When there are multiple observations in each category, it also uses bootstrapping to compute a confidence interval around the estimate and plots that using error bars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = sns.load_dataset(\"titanic\")\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"sex\", y=\"survived\", hue=\"class\", kind=\"bar\", data=titanic);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A special case for the bar plot is when you want to show the number of observations in each category rather than computing a statistic for a second variable. This is similar to a histogram over a categorical, rather than quantitative, variable. In seaborn, it's easy to do so with the `countplot()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"deck\", kind=\"count\", palette=\"ch:.25\", data=titanic);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both `barplot()` and `countplot()` can be invoked with all of the options discussed above, along with others that are demonstrated in the detailed documentation for each function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y=\"deck\", hue=\"class\", kind=\"count\",\n",
    "            palette=\"pastel\", edgecolor=\".6\",\n",
    "            data=titanic);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Point plots\n",
    "\n",
    "An alternative style for visualizing the same information is offered by the `pointplot()` function. This function also encodes the value of the estimate with height on the other axis, but rather than showing a full bar, it plots the point estimate and confidence interval. Additionally, `pointplot()` connects points from the same `hue` category. This makes it easy to see how the main relationship is changing as a function of the hue semantic, because your eyes are quite good at picking up on differences of slopes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"sex\", y=\"survived\", hue=\"class\", kind=\"point\", data=titanic);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the categorical functions lack the `style` semantic of the relational functions, it can still be a good idea to vary the marker and/or linestyle along with the hue to make figures that are maximally accessible and reproduce well in black and white:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"class\", y=\"survived\", hue=\"sex\",\n",
    "            palette={\"male\": \"g\", \"female\": \"m\"},\n",
    "            markers=[\"^\", \"o\"], linestyles=[\"-\", \"--\"],\n",
    "            kind=\"point\", data=titanic);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting “wide-form” data\n",
    "\n",
    "While using “long-form” or “tidy” data is preferred, these functions can also by applied to “wide-form” data in a variety of formats, including pandas DataFrames or two-dimensional numpy arrays. These objects should be passed directly to the `data` parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=iris, orient=\"h\", kind=\"box\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, the axes-level functions accept vectors of Pandas or numpy objects rather than variables in a `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=iris.species, y=iris.sepal_length);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To control the size and shape of plots made by the functions discussed above, you must set up the figure yourself using matplotlib commands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D4F5dztQ005ypSeNxHnDVSuh02wE9E3hf9/P3AOeM8Dw3VNVdVXUI2MvgnnySOoaaNB5h9duqrPz8frrfzW4D6JOGzhneF/EgXm2RvoehJo3HNcBLug1s6S4//hODHesBLgE+0z2+Azi7e/wzwIkjPP8ysOlYFSsdr/xXnjQGVXVLksuBv09ykMGO9K8E3pnkNxncGfjl3elvBz6U5AYGYXjvCF3sBj6WZH9VPffYj0A6PrikX5LUDC8/SpKaYahJkpphqEmSmmGoSZKaYahJkpphqEmSmmGoSZKa8T8o2469hduOfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7, 3))\n",
    "sns.countplot(y=\"deck\", data=titanic, color=\"c\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the approach you should take when you need a categorical figure to happily coexist in a more complex figure with other kinds of plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Showing multiple relationships with facets\n",
    "\n",
    "Just like `relplot()`, the fact that `catplot()` is built on a `FacetGrid` means that it is easy to add faceting variables to visualize higher-dimensional relationships:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 485.359x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"day\", y=\"total_bill\", hue=\"smoker\",\n",
    "            col=\"time\", aspect=.6,\n",
    "            kind=\"swarm\", data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For further customization of the plot, you can use the methods on the `FacetGrid` object that it returns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x324 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x=\"fare\", y=\"survived\", row=\"class\",\n",
    "                kind=\"box\", orient=\"h\", height=1.5, aspect=4,\n",
    "                data=titanic.query(\"fare > 0\"))\n",
    "g.set(xscale=\"log\");"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
